【摘要】 特征选择是模式识别领域的重要环节。本文提出了一种改进的类别依赖型特征选择技术,通过事先选定参数m,自动选择出在文中准则下各个类各自最能区别于其他类的k_x维特征。将各类所选择特征的并集作为BP神经网络的输入结点,进而用待识别样本进行分类识别。实验结果说明,该类特征选择方法能够选择出对每一类而言最能区分于其他类的特征。与选择所有特征相比,用该法进行特征选择以后的BP神经网络分类识别有着较高的正确识别率。此外,本文还利用相关分析方法在预处理过程中剔除了线性相关的冗余特征。更多还原

【Abstract】 Feature selection is an important part of pattern recognition.This paper provides an improved technology of feature selection tied to different classes.Through adjusting m,k_x features of a certain class x can be chosen out automatically.We combine all the chosen features tied to different classes and take them as the inputs of BP neural network for classification.The result of the experiment shows that this method can choose out the most representative and the most separable features of different classes.Compared with others the correct classification ratio is high as well.Besides,all the linear correlated features have been removed during preprocessing.更多还原